How to Identify Influence Leaders in Social Media: Zsolt Katona

Feb. 27 (Bloomberg) -- The currency of social media is
influence. Credit-card companies offer rewards to customers with
a high influence score, airlines give such people free flights,
and some employers make job offers dependent on those ratings.

So how do they find opinion leaders? How do they determine
a person’s influence? Where do these scores come from?

Recent research I conducted with Peter Zubcsek and Miklos
Sarvary examined personal influences in an online social network
in a European county. Building on an almost 300-year-old
discipline, graph theory, we studied how members attracted their
friends to join the network.

We found that, besides the number of one’s friends already
using the service, the interconnectedness of these friends is
just as relevant in making a person join. In other words,
whether your friends know one another makes a big difference in
how much they are able to influence you. This force is so strong
that when five of your friends are already users, the effect of
adding one friend as a user is the same as having one extra
friendship between your friends.

Network structure matters. But how do we identify the
influencers? Because we had access to data on all the
friendships in the network, in addition to some demographic
variables, we were able to determine how one person’s position
in the network affected his or her power of influence. A common
measure of influence is simply the number of connections someone
has, but that isn’t always the correct way to pinpoint leaders.

Highly Connected

We found that highly connected individuals have less
influence on each of their friends on average. Having more
friends can make your total reach and influence higher when it
comes to compelling others to sign up for a free service, for
example. But this isn’t necessarily so when it’s a matter of
swaying people who are making an important decision. If you are
in the process of buying a new car or a home, you are likely to
ignore the advice of your highly connected but not so close
friends. The lesson is that more connections don’t always mean
higher influence.

To determine a person’s influence, it isn’t enough to
simply count the number of connections they have. More
meaningful are the communities or groups that people belong to
within social networks. Most users have a separate but
potentially overlapping set of friends that includes work
colleagues, family and schoolmates.

To understand the mechanics of a social network it is
imperative to be aware of these communities, but they are often
impossible to observe. For example, in telecommunications
networks, all calls are recorded, resulting in vast amounts of
data on one-to-one connections, but no information on how the
callers know each other.

We discovered that social-network analysis makes it
possible to find these communities using network data, and we
used our algorithm to identify communities in a number of phone
networks. (See attached graphic.)

Finding communities in a social network allows companies to
identify influential individuals and to better understand their
role. Some people are at the center of communities, possibly
having a strong influence on their peers. Others may be on the
periphery, but still have an important role in connecting
different communities. Mapping out communities helps us
understand how information travels in a network.

We conducted an experiment that illustrates the benefits of
community-based social-network analysis. We obtained call data
records from a mobile-phone provider in Asia that was trying to
expand its loyalty program. Using our social-network analysis
methods, we identified a target group of people who were active
in a community for a text-message campaign that asked customers
to join the program. We then compared the response rate to a
control group that was targeted by the provider using only
traditional marketing tools.

Call Data

There was a substantial difference in response rates (9.2
percent versus 5.4 percent), proving the benefits of detailed
network analysis. Moreover, a unique code sent with the messages
allowed us to track customers who received a forwarded text
message from a friend and not directly from the provider. Once
again, our target outperformed the control group (6.3 percent
versus 4.1 percent).

With all the network data available to marketers, it is
important to appreciate the value in finding influential
consumers and communities. We are just at the beginning of a
long journey to fully understand the role of influence in social
networks, but it’s already clear that simple metrics aren’t
sufficient.

(Zsolt Katona is an assistant professor of marketing at the
University of California, Berkeley’s Haas School of Business and
a contributor to Business Class. The opinions expressed are his
own.)